Workload placement for virtual gpu enabled systems

ABSTRACT

Disclosed are aspects of workload selection and placement in systems that include graphics processing units (GPUs) that are virtual GPU (vGPU) enabled. In some aspects, workloads are assigned to virtual graphics processing unit (vGPU)-enabled graphics processing units (GPUs). A number of vGPU placement neural networks are trained to maximize a composite efficiency metric based on workload data and GPU data for the plurality of vGPU placement models. A combined neural network selector is generated using the vGPU placement neural networks, and utilized to assign a workload to a vGPU-enabled GPU.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims the benefit of U.S.patent application Ser. No. 16/742,108, entitled “WORKLOAD PLACEMENT FORVIRTUAL GPU ENABLED SYSTEMS,” and filed Jan. 14, 2020, which is herebyincorporated by reference in its entirety.

BACKGROUND

A cluster can include a collection of hosts in which processor, memory,storage, and other hardware resources are aggregated for utilization. Ahost is capable of running one or more virtual computing instances, suchas virtual machines and other workloads. A workload can include anoperating system (OS) running one or more applications that can utilizehost resources. Placing workloads with graphics processing requirementswithin a datacenter with heterogeneous systems can pose a number ofissues. The systems can have distinct types of accelerators: GPU, FPGAand application specific integrated circuits. A host machine in adatacenter may have one or more of these accelerators. Assignment of aworkload to a host can depend on matching the requirements of the taskto the available accelerators on the machine. If a workload has graphicsprocessing requirements, it can be placed on a host with a graphicsaccelerator that meets the graphics processing demands of the workload.

Virtualized GPUs (vGPUs) present opportunities to improve resourceutilization with the potential benefit of ease of management. Whilesystem administrators can choose a placement model for a vGPU-enabledsystem, the placement model can be efficient for one scenario whileinefficient for others within an evolving heterogeneous datacenter. Thiscan result in sub-optimal placement of virtual machines, unbalancedhosts, network saturation, overloading of network links, and inefficientutilization of available resources.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the present disclosure can be better understood withreference to the following drawings. The components in the drawings arenot necessarily to scale, with emphasis instead being placed uponclearly illustrating the principles of the disclosure. Moreover, in thedrawings, like reference numerals designate corresponding partsthroughout the several views.

FIG. 1 is a block diagram illustrating an example of a networkedenvironment that includes a computing environment, a client device, andother components in communication over a network.

FIG. 2 is a drawing that illustrates an example neural networkconfiguration utilized by components of the networked environment.

FIG. 3 is a drawing that illustrates an example neural networkconfiguration utilized by components of the networked environment.

FIG. 4 is a drawing that illustrates an example neural networkconfiguration utilized by components of the networked environment.

FIG. 5 is a flowchart that illustrates an example of functionalitiesperformed using components of the networked environment.

DETAILED DESCRIPTION

The present disclosure relates to workload placement in systems thatinclude graphics processing units (GPUs) that are virtual GPU (vGPU)enabled. The vGPU enabled systems can include data centers, cloudcomputing services, and other computing environments. These systems canprocess vGPU requests of virtual machines and other workloads and assignthe vGPU requests to GPUs in a vGPU enabled system. As a result,multiple workloads can use a vGPU enabled GPU at the same time. Existingsystems can result in sub-optimal placement of workloads by selection ofa placement model that is only effective for a particular count of GPUsor hardware configuration, vGPU scheduling policy, and workload arrivalrate. However, the mechanisms described herein can result in efficientselection and placement of workloads on vGPUs for multiple hardwareconfigurations, vGPU scheduling policies, and arrival rates.

With reference to FIG. 1 , an example of a networked environment 100 isshown. The networked environment 100 can include a computing environment103, various clusters 106, and one or more client devices 108 incommunication with one another over a network 109. The network 109 caninclude wide area networks (WANs) and local area networks (LANs). Thesenetworks can include wired or wireless components, or a combinationthereof. Wired networks can include Ethernet networks, cable networks,fiber optic networks, and telephone networks such as dial-up, digitalsubscriber line (DSL), and integrated services digital network (ISDN)networks. Wireless networks can include cellular networks, satellitenetworks, Institute of Electrical and Electronic Engineers (IEEE) 802.11wireless networks (i.e., WI-FI®), BLUETOOTH® networks, microwavetransmission networks, as well as other networks relying on radiobroadcasts. The network 109 can also include a combination of two ormore networks 109. Examples of networks 109 can include the Internet,intranets, extranets, virtual private networks (VPNs), and similarnetworks. As the networked environment 100 can serve up virtual desktopsto end users, the networked environment 100 can also be described as avirtual desktop infrastructure (VDI) environment.

The computing environment 103 can include host resources 113. The hostresources 113 can include processors, GPUs 115, data stores 116, andother hardware resources installed in hosts or physical machines of thecomputing environment 103. In some examples, the computing environment103 can include an enterprise computing environment that includeshundreds or even thousands of physical machines, virtual machines, andother software implemented in devices stored in racks, distributedgeographically and connected to one another through the network 109. Itis understood that any virtual machine or virtual appliance isimplemented using at least one physical device.

The computing environment 103 can include, for example, a server or anyother system providing computing capability and other host resources113. Alternatively, the computing environment 103 can include one ormore computing devices that are arranged, for example, in one or moreserver banks, computer banks, clusters, or other arrangements. Thecomputing environment 103 can include a grid computing resource or anyother distributed computing arrangement. The computing devices can belocated in a single installation or can be distributed among manydifferent geographical locations. Although shown separately from theclusters 106, in some examples, the clusters 106 can be a portion of thecomputing environment 103. Various applications can be executed on thecomputing environment 103. For example, a scheduling service 120 can beexecuted by the computing environment 103. Other applications, services,processes, systems, engines, or functionality not discussed in detailherein may also be executed or implemented by the computing environment103.

The computing environment 103 can include or be operated as one or morevirtualized computer instances. For purposes of convenience, thecomputing environment 103 is referred to herein in the singular. Eventhough the computing environment 103 is referred to in the singular, itis understood that a plurality of computing environments 103 can beemployed in the various arrangements as described above. As thecomputing environment 103 communicates with the clusters 106 and clientdevices 108 for end users over the network 109, sometimes remotely, thecomputing environment 103 can be described as a remote computingenvironment 103 in some examples. Additionally, in some examples, thecomputing environment 103 can be implemented in hosts of a rack of thecomputer clusters 106, and can manage operations of a virtualizedcomputing environment.

The GPUs 115 can be vGPU-enabled, or support vGPUs. For example, NVIDIA®vGPU solutions can allow multiple virtual machines, containers, orworkloads 118, to share a GPU 115 with a balance among performance,security and isolation. In vGPU mode or mediated pass-through mode,workloads 118 time-share the GPU 115 resources by time-slicing andhardware preemption based on vGPU-enabled architectures such as theNVIDIA® Pascal architecture. In any given time slice, only one workload118 runs on a GPU 115. All GPU cores of the GPU 115 are given to thisworkload 118 during the time slice, even if it does not use all of thecores. The GPU internal memory can be statically partitioned based on avGPU profile. For example, NVIDIA® Tesla P100 16GB GPU 115 can support 1GB, 2 GB, 4 GB, 8 GB, and 16 GB vGPU profiles. The profiles can equallydivide the total GPU memory of the GPU 115 into sections or partitionsaccording to the memory size of the vGPU profile. When configured with a1 GB profile, an NVIDIA® Tesla P100 can support up to 16 workloads 118,each provided with 1 GB of the total 16 GBs of the NVIDIA® Tesla P100GPU 115. The NVIDIA® Tesla P100 GPU 115 can support up to 8 workloads118 using the 2 GB profile, 4 workloads 118 using the 4 GB profile, 2workloads 118 using the 8 GB profile, and a single workload 118 usingthe 16 GB profile.

An NVIDIA® Tesla P40 24 GB GPU 115 can support 1 GB, 2 GB, 3 GB, 4 GB, 6GB, 8 GB, 12 GB, and 24 GB vGPU profiles. When configured with a 1 GBprofile, an NVIDIA® Tesla P40 can support up to 24 workloads 118, eachprovided with 1 GB of the total 24 GBs of the NVIDIA® Tesla P40 GPU 115.The NVIDIA® Tesla P40 GPU 115 can support up to 12 workloads 118 usingthe 2 GB profile, 8 workloads 118 using the 3 GB profile, 6 workloads118 using the 4 GB profile, 4 workloads 118 using the 6 GB profile, 2workloads 118 using the 12 GB profile, and a single workload 118 usingthe 24 GB profile.

NVIDIA® vGPU can include three policies that determine how time slicesare allocated, including best effort, equal share, and fixed share. Inbest effort policy, each workload 118 can use GPU cycles until its timeslice is over, or until its job queue is empty. That is, GPU cycles aredistributed among all workloads 118 that are running CUDA applications.For equal share, the amount of cycles given to each vGPU 112 isdetermined by the current number of workloads 118, regardless of whetherthese workloads 118 are running CUDA or GPU-utilizing applications ornot. For fixed share, the amount of cycles given to each vGPU 112 isdetermined by the total number of supported workloads 118 under thegiven profile, regardless if other workloads 118 are powered on or not.In some examples, the best-effort policy can be chosen while using thescheduling service 120.

The data store 116 can include memory of the computing environment 103,mass storage resources of the computing environment 103, or any otherstorage resources on which data can be stored by the computingenvironment 103. In some examples, the data store 116 can include one ormore relational databases, object-oriented databases, hierarchicaldatabases, hash tables or similar key-value data stores, as well asother data storage applications or data structures. The data stored inthe data store 116, for example, can be associated with the operation ofthe various services or functional entities described below. Forexample, workloads 118, the scheduling service 120, GPU data 125,workload data 126, and vGPU placement models 129 can be stored in thedata store 116.

The scheduling service 120 can work in conjunction with a hypervisor ofa host in the computing environment 103 to assign vGPU requests of theworkloads 118 to GPUs 115. Assignment of a vGPU request can cause theworkload 118, or a portion of the workload 118, to be executed using avGPU of a GPU 115. The scheduling service 120 can identify a graphicsprocessing requirement for a workload 118 as a vGPU request that is tobe executed or performed using the host resources 113. The schedulingservice 120 can handle the graphics processing requirement or vGPUrequest of the workload 118 using a vGPU-enabled GPU 115. The schedulingservice 120 can utilize the vGPU placement models 129 to optimizeselection and placement of workload 118 vGPU requests to GPUs 115. Thescheduling service 120 can work in conjunction with the hypervisor togenerate a vGPU for the vGPU request, and assign the vGPU request or theassociated workload 118 for execution using a vGPU-enabled GPU 115.

The vGPU placement models 129 can include first-come-first-serve (FCFS),longest-first, longest-wait-first, random, shortest-first, andbin-packing heuristic, among other vGPU placement models 129. The FCFSvGPU placement model 129 can, on each simulation or actual clock tick,try to place the first workload 118 in the wait queue, then the second,and so on. The longest-first vGPU placement model 129 can, on each clocktick, attempt to place the workload 118 with the longest run time first,then the one with the second longest run time, and so on. Thelongest-wait-first vGPU placement model 129 can, on each clock tick, tryto place the workload 118 with the longest wait time first, then the onewith the second longest wait time, and so on. The random vGPU placementmodel 129 can, on each clock tick, randomly select a workload 118 andtry to place it, then it selects another one at random and so on. Theshortest-first placement model 129 can, on each clock tick, try to placethe workload 118 with the shortest run time first, then the one with thesecond shortest run time, and so on. Bin-Packing vGPU placement model129 can, for each workload 118 in the arrival queue that can be placedon a GPU 115, compute the composite efficiency metric 127 if theworkload 118 were to be placed. The computed composite efficiency metric127 values are sorted and the workloads 118 with the highest compositeefficiency metric 127 values are placed till available slots in the GPUs115 are exhausted. Different vGPU placement models 129 show the bestcomposite efficiency metric 127 for different configurations. Forexample, the random selection can show the best composite efficiencymetric 127 when the problem is under constrained with a large GPU countand low arrival rates. The bin-packing heuristic can have better resultswith a small GPU count or high arrival rates. So, for a vGPU-enabledcloud with varying arrival rates or different number of available GPUs,a single heuristic or vGPU placement model 129 can be ineffective forall possible or likely configurations.

GPU data 125 can represent information related to GPUs 115, as well asrelated hardware resources 113. GPU data 125 can include informationsuch as the amount of GPU memory of the GPU 115, a set of supported vGPUprofiles for the GPU 115, and a GPU configuration status. The GPUconfiguration status can indicate whether or not the GPU 115 iscurrently configured with a particular vGPU profile. If the GPU 115 isconfigured, the configuration status can also indicate the configuredvGPU profile of the GPU 115. GPU data 125 can also include informationrelated to the workloads 118 currently executing on each GPU 115, aswell as workloads 118 scheduled or slated to be executed. GPU data 125can include a record of the workloads 118 assigned to each GPU 115. GPUdata 125 can also include vGPUs of the GPUs 115. For each vGPU, the GPUdata 125 can include a GPU memory reservation and availability status.The GPU memory reservation can be an amount of GPU memory of the vGPU,according to a configured vGPU profile of the associated GPU 115.

Workload data 126 can represent information related to workloads 118.Workload data 126 can include a record of all vGPU requests for theworkloads 118. A vGPU request can include a graphics processing workloador graphics processing requirement of a workload 118. Workload data 126can include an identifier or name of each workload 118, and anidentifier or location of a GPU 115 where a vGPU request or workload 118is being processed or executed.

A composite efficiency metric 127 can be used to compare differentworkload or vGPU placement models 129 to select and place jobs, tasks,or workloads 118 on GPUs 115. The composite efficiency metric 127 caninclude a product of Utilization of the GPUs 115 and time spent by aworkload 118 waiting for a GPU 115 to become available. Specifically, ina system with ‘r’ GPUs, the Utilization, ‘U’, of the GPUs can beexpressed as

${U = {\frac{1}{r}\left( {{\sum}_{i = 1}^{r}\frac{R_{C}^{i}}{C}} \right)}},$

and 0≤U≤1, where R_(c) ^(i)=Busy cycles for RunQ, R^(i) and C=Total #simulation cycles.

The metric, ‘time spent waiting’ should be minimized for an optimalsolution. To make this a maximization problem, this metric can beutilized by defining a ratio E of time spent executing to the total timespent in the system, which can be expressed as

${E = \left( \frac{{\sum}_{i = 1}^{m}J_{r}^{i}}{{\sum}_{i = 1}^{m}\left( {J_{r}^{i} + J_{w}^{i}} \right)} \right)},$

and 0≤E≤1, where J_(r) ^(i)=Cycles in RunQ, r for task, J^(i), and J_(w)^(w)=Cycles in WaitQ for task, J^(i).

The term J_(r) ^(i) can depend on a number of cycles during which thejobs/task was executing, not just the number of cycles for which theworkload 118 was in the GPU queue. This distinction is valuable becausewhen a GPU is multiplexed among many workloads 118 only one workload 118executes at a time and it does not execute when the current time slicedoes not belong to it. Further, the different GPU scheduling policiescan handle time slices differently as explained earlier. In other words,a fixed-share scheduling policy for a GPU 115 can have a different valuefor this ratio than a best-effort scheduling policy because manytime-slices could be left idle with fixed-share scheduling as comparedto best-effort scheduling. If a workload 118 is suspended andsubsequently resumed, the clock cycles spent during the suspend and theresume operations can be included in the term, J_(r) ^(i). The timebetween completion of suspend and the start of resume is spent in thewaiting queue and can be included in the term J_(w) ^(i). The compositeefficiency metric 127, or CEM, can be defined as E=½ (U×E), and 0≤CEM≤1.As a result, CEM calculations based on simulated or live workloads canbe utilized to rank the available vGPU placement models 129, train vGPUplacement neural networks 131 such as dense neural networks (DNNs). ThevGPU placement neural networks 131 and combinations of multiple vGPUplacement neural networks 131 can also maximize the CEM criterion forselection and placement of live workloads 118 on GPUs 115 of thecomputing environment 103. Maximizing the composite efficiency metric127 can maximize the utilization of GPUs 115 while minimizing wait time.From a cloud service provider's perspective, this can improveutilization efficiency of existing hardware as compared to existingplacement methods, increase revenue, and improve customer satisfaction.

Workload parameters 128 can include parameters related to live orsimulated execution of a workload 118 in the computing environment 103.The workload parameters 128 can be provided as input parameters for avGPU placement neural network 131 in order to select and place aworkload 118 on a vGPU-enabled GPU 115. Workload parameters 128 caninclude, or be generated using, the GPU data 125, workload data 126,composite efficiency metric 127, and other information. For example,workload parameters 128 can include:

-   -   1. The composite efficiency metric 128 for a workload 118 scaled        by the geometric mean of the composite efficiency metric 128 for        all the workloads 118 currently on the arrival queue.    -   2. The workload type of the workload 118. A workload 118 can        include a type or classification associated with a purpose or        activity related to the workload. For example, workload types        can include machine learning inference workload type, machine        learning training workload type, computer aided drafting (CAD)        workload type, and other types of workloads 118.    -   3. Minimum graphics memory required for the workload 118. This        minimum graphics parameter can be expressed as a fraction of the        maximum Pascal DRAM of a particular GPU 115. For example, a        fraction of 24 GB for a 24 GB GPU 115.    -   4. Time a workload 118 has spent being suspended in the        computing environment 103.    -   5. Time a workload 118 has spent being resumed in the computing        environment 103.    -   6. Time spent in the arrival queue, or wait time a workload 118        spends prior to being placed in the computing environment 103.    -   7. Time a workload 118 has spent executing thus far.    -   8. Run time remaining before a workload 118 completes or is        expected to complete.    -   9. Product of “wait time” and run time remaining before a        workload 118 completes.    -   10. GPU DRAM requested by the workload 118 as a fraction of        total available GPU DRAM in the vGPU cloud or computing        environment 103.    -   11. Approximate estimate of the probability of selection and        placement of the workload 118.    -   12. Accurate estimate of the probability of selection and        placement of the workload 118.

The workload parameters 128 provided as input parameters to a vGPUplacement neural network 131 can include all or a subset of theseworkload parameters 128. The example workload parameters 128 areprovided for illustrative purposes, and are not intended as anexhaustive list.

A combined neural network selector 132, or a combinedneural-network-based vGPU workload selector, can be generated byconfiguring, training, and combining vGPU placement neural networks 131.Specific vGPU placement neural networks 131 are discussed further below.The vGPU placement neural networks 131 can be combined in a number ofways into a combined neural network selector 132. For example, thecombined neural network selector 132 can be generated based on alogical-OR combination operation or a scaled add combination operationperformed using trained vGPU placement neural networks 131.

A logical-OR combined neural network selector can take a predictionvector for each of a number of vGPU placement neural networks 131. Ifany one vGPU placement neural network 131 selects a workload 118, thelogical-OR combined selector can select and place that task on a GPU 115queue. While the logical-OR combined selector can be simple, it tends tomark more tasks as selected than there are places available in thecloud.

A scaled-add combined neural network selector can multiply theprediction vector for each of the vGPU placement neural networks 131 bya precision of that vGPU placement neural network 131. This scales eachprediction vector by the probability that a workload 118 marked selectedby a vGPU placement neural network. The scaled prediction vectors forthe vGPU placement neural networks 131 or DNNS are then added, androunded or and clipped to the range [0, 1]. This has the effect ofsetting every value at and below 0.5 to 0 and every value greater than0.5 to 1, no matter how large it might be. So, even the maximum possiblevalue of 3.0 is forced to 1. The scaled-add combined selector ofcombining the prediction vectors never generates more selected workloads118 than there are places in the vGPU enabled cloud, so the scaled-addcombined prediction vector can be implemented unpruned. Either DNN-basedselector can be implemented in a live datacenter to select workloads 118and place on vGPU-enabled GPUs 115, for example, in GPU queue of avGPU-enabled GPU 115.

A hypervisor, which may sometimes be referred to as a virtual machinemonitor (VMM), can be an application or software stack that allows forcreating and running workloads 118, and performing the workloads 118using hardware resources of the computing environment 103. Thescheduling service 120 can work in conjunction with the hypervisor toexecute the workloads 118 on hardware resources that include the GPUs115. A vGPU manager component can be installed and executed in thehypervisor layer and can virtualize the underlying physical GPUs 115.For example GPUs 115, including NVIDIA® Pascal and others, can offervirtualization for both graphics and GPGPU (CUDA) applications.

A hypervisor can be configured to provide guest operating systems with avirtual operating platform, including virtualized hardware devices orresources, and to manage the execution of guest operating systems withina virtual machine execution space provided on the host machine by thehypervisor. In some instances, a hypervisor can be a type 1 or baremetal hypervisor configured to run directly on a host machine in orderto control and manage the hardware resources 153. In other instances,the hypervisor can be a type 2 or hosted hypervisor implemented as anapplication executed by an operating system executed by a host machine.Examples of different types of hypervisors include ORACLE VM SERVER™,MICROSOFT HYPER-V®, VMWARE ESX™ and VMWARE ESXi™, VMWARE WORKSTATIONT™,VMWARE PLAYER™, and ORACLE VIRTUALBOX®.

The scheduling service 120 can train vGPU placement neural networks 131by analyzing results from live workloads 118 or results from a simulator121 that simulates selection and placement of workloads 118 onvGPU-enabled GPUs 115. The simulator 121 can include two logicalcomponents. One component can generate workloads 118 to be placed on aGPU 115. The second component can model a vGPU cloud as a set of queuesand model the placement of workloads 118 in the simulated vGPU cloudusing any one of the vGPU placement models 129 or vGPU placement neuralnetworks 131 under consideration. The load generator component cangenerate workloads 118 using a user-provided or simulated arrival rateparameter, lambda. The simulator 121 can assume that the inter-arrivaltime between workloads 118 is exponentially distributed. Each time a jobis created, the simulator 121 can put it in one of a predetermined setof workload categories or types using a uniform distribution or anotherdistribution.

The workload categories can include a machine learning inferenceworkload type, machine learning training workload type, and CAD workloadtype, among others. Machine learning inference workloads 118 can performinference using a machine learning model that has been trained. Machinelearning training workloads 118 can train a machine learning model tocomplete a task or decision. CAD workloads 118 can be those that run CADsoftware. Additional workloads 118 can be other interactive userinterface type workloads 118, which can generally behave as discussedfor CAD workloads 118. Each of these categories has distinctcharacteristics.

The machine learning inference workloads 118 can include a run-time lessthan one second and request either a P40-12Q or a P40-24Q profile. Themachine learning training workloads 118 request a P40-12Q or a P40-24Qprofile, and can have a run time of forty-five minutes, forconvolutional neural network (CNN) jobs, to six hours for recurrentneural network (RNN) jobs. CAD workloads 118 can be interactiveworkloads that provide a user interface for a user or simulated useractions. Since CAD workloads can be user-driven, they can be createdbetween 8 AM and LOAM each day and complete between 4 PM and 6 PM. Theprofile request for CAD workloads 118 can be uniformly distributed overall available P40 profiles or other available profiles. The profileassociated with a workload 118 is an important constraint on when andwhere it can be placed. Machine learning training workloads 118 can beconsidered “batch” jobs, which can be “suspended” and “resumed” asneeded to make space for other workloads 118. A suspend resume model orrule can be utilized to decide when to “suspend” and “resume” machinelearning training workloads 118. Machine learning training workloads 118are run only when they do not compete with CAD workloads 118 for vGPUs.The simulator 121 can include one or more arrival queues for arrivingworkloads 118 requesting vGPU of the GPUs 115.

Once the simulator 121 creates workloads 118 using an exponentialdistribution with a predetermined or user defined inter-arrival rate,they are placed into one of the three categories described above using auniform random variable. The arrival rate can be varied or changedperiodically for a simulation, or for further training. In someexamples, there can be a probability of 50% for a job created between 8am-10 am to be a machine learning inference workload 118, otherwise itcan be a CAD workload 118. Outside this time window, a job can be amachine learning inference workload 118 with 98% probability and with 2%probability to be a machine learning training workload 118. Theseprobabilities are for illustrative purposes, and can be in anydistribution. Once a workload 118 has been created, it can be enqueuedin an arrival queue. On every simulation clock tick, one or moreworkloads 118 can be selected from the arrival queue using a vGPUplacement model 129 and placed into one of several GPU queues; one GPUqueue for each GPU 115 in the simulated cloud. The GPU count or numberof GPUs 115 in the cloud can be predetermined or selected by a user whenthe simulation is started. Each GPU queue can include the list ofworkloads 118 currently being executed by vGPUs of a GPU 115.

Since the Nvidia vGPU solution supports multiple vGPU profiles, eachwith a different maximum number of workloads 118, the size of a GPUqueue is limited by the maximum number of workloads 118 (or virtualmachines since each workload 118 can run in its own virtual machine)that can be accommodated at a configured vGPU profile. The vGPU profileof a GPU queue is defined by the vGPU profile of the first workload 118that is scheduled on that queue. Workloads 118 that arrive second, thirdor later at a GPU queue may join the queue only if two conditions areboth satisfied: The vGPU profile of the incoming workload 118 matchesthe vGPU profile of the GPU queue. The current length of the GPU queueis strictly less than the maximum allowed number of jobs (VMs) for theGPU queue's profile. Once a GPU queue empties out because the GPU hascompleted all the jobs assigned to it, its profile can be forgotten.

The GPU queue will not have any profile till a workload 118 is assignedto it, at which time it assumes the profile of that workload 118.Another way to erase the profile of a GPU queue is to suspend all theworkloads 118 on that GPU queue thereby emptying it out and clearing itsprofile. This mechanism of erasing the profile of a GPU queue createsanother dimension to be handled by placement algorithms. Suspending andsubsequently resuming jobs as a placement technique can be used in twodifferent ways. First, one or more jobs can be suspended to make way ona GPU queue for an incoming workload 118 with the same vGPU profile.Second, all the workloads 118 on a GPU queue can be suspended to makeway for any new workload 118, regardless of its vGPU profile. These twodynamic suspend-resume techniques allow placement algorithms significantflexibility at the cost of increased complexity. Once a workload 118 hasbeen placed on a GPU queue, the GPU 115 controls its execution using oneof three different scheduling policies including fixed-share scheduling,equal-share scheduling, and best effort scheduling. The policy that isused in a simulation is predetermined or selected by a user, and can bevaried or changed for further simulation.

The simulator 121 can run simulations using each of the vGPU placementmodels 129 while varying the workloads 118 as discussed, and for variousnumbers of GPUs 115, arrival rates, and GPU scheduling policies. Theresulting GPU data 125 and workload data 126 for each of the vGPUplacement models 129 can be used to train vGPU placement neural networks131. While data from a simulation can be utilized, live workload data126 and live GPU data 125 from actual workloads performed for anenterprise can also be analyzed and used to train vGPU placement neuralnetworks 131. The vGPU placement neural networks 131 can be combined togenerate a combined neural network selector 132.

FIG. 2 is a drawing that illustrates an example neural networkconfiguration 231 for a vGPU placement neural network 131. The neuralnetwork configuration 231 can be a configuration for a dense neuralnetwork, or another type of vGPU placement neural network 129. Theneural network configuration 231 can include two sets of “X” layers,where each layer includes “Y” nodes. The neural network configuration231 can take “N” workload parameters 128 as inputs, and can output adecision of whether to select a workload 118 for placement on avGPU-enabled GPU 115 based on the trained vGPU placement neural network131 having the neural network configuration 231. In some examples, thenumber of layers can be X=4, and the number of nodes can be Y=48. Theinput parameters can include all or a subset of the twelve workloadparameters 128 discussed above.

FIG. 3 is a drawing that illustrates an example neural networkconfiguration 331 for a vGPU placement neural network 131. The neuralnetwork configuration 331 can be a configuration for a dense neuralnetwork, or another type of vGPU placement neural network 129. Theneural network configuration 331 can include two sets of “X” layers. Aset of X input layers can feed a set of X output layers. Each layer ofthe input layers can include “2Y” nodes. Each layer of the output layerscan include “Y” nodes. The neural network configuration 331 can take “N”workload parameters 128 as inputs, and can output a decision of whetherto select a workload 118 for placement on a vGPU-enabled GPU 115 basedon the trained vGPU placement neural network 131 having the neuralnetwork configuration 331. In some examples, the number of layers can beX=4, and the number of nodes can be Y=24, so that each of the inputlayers includes 48 nodes, and each of the output layers includes 24nodes. The input parameters can include all or a subset of the twelveworkload parameters 128 discussed above.

FIG. 4 is a drawing that illustrates an example neural networkconfiguration 431 for a vGPU placement neural network 131. The neuralnetwork configuration 431 can be a configuration for a dense neuralnetwork, or another type of vGPU placement neural network 129. Theneural network configuration 431 can include three sets of “X” layers. Aset of X input layers can feed a set of X intermediate layers, which canfeed a set of X output layers. Each layer of the input layers caninclude “2Y” nodes. Each layer of the output layers can include “Y”nodes. Each layer of the intermediate layers can include a number ofnodes that is greater than or equal to Y and less than or equal to 2Y.The neural network configuration 431 can take “N” workload parameters128 as inputs, and can output a decision of whether to select a workload118 for placement on a vGPU-enabled GPU 115 based on the trained vGPUplacement neural network 131 having the neural network configuration431. In some examples, the number of layers can be X=4, and the numberof nodes can be Y=48, so that each of the input layers includes 96nodes, and each of the output layers includes 48 nodes. In this example,each of the intermediate layers can include 64 nodes, or any number ofnodes from 48 to 96. The input parameters can include all or a subset ofthe twelve workload parameters 128 discussed above.

FIG. 5 shows an example flowchart 500, describing steps that can beperformed by instructions executed by the computing environment 103.Generally, the flowchart 500 describes how the scheduling service 120and other instructions of the computing environment 103 can optimizeselection and placement of workloads 118 on vGPU-enabled GPUs 115,according to a vGPU placement neural network 131 or a combined neuralnetwork selector 132.

In step 503, the scheduling service 120 can configure vGPU placementneural networks 131. The scheduling service 120 can generate one or moreconfigurations for vGPU placement neural networks 131. For example, thescheduling service 120 can generate the neural network configuration231, the neural network configuration 331, and the neural networkconfiguration 431.

In step 506, the scheduling service 120 can monitor simulated or liveworkloads 118 assigned to vGPUs using a variety of vGPU placement models129. In the example of simulated workloads 118, the simulator 121 cancreate workloads 118 with an exponential distribution and according to apredetermined set of probabilities. For each of the vGPU placementmodels, the simulator 121 can run for a predetermined amount of time,according to a predetermined arrival rate and a predetermined GPU countto identify GPU data 125 and workload data 126. GPU data 125 can includeinformation such as the amount of GPU memory of the GPU 115, a set ofsupported vGPU profiles for the GPU 115, and a GPU configuration status.The GPU configuration status can indicate whether or not the GPU 115 iscurrently configured with a particular vGPU profile. If the GPU 115 isconfigured, the configuration status can also indicate the configuredvGPU profile of the GPU 115. GPU data 125 can also include informationrelated to the workloads 118 currently executing on each GPU 115, aswell as workloads 118 scheduled or slated to be executed. GPU data 125can include a record of the workloads 118 assigned to each GPU 115. GPUdata 125 can also include vGPUs of the GPUs 115. For each vGPU, the GPUdata 125 can include a GPU memory reservation and availability status.

Workload data 126 can include an identifier or name of each workload118, and an identifier or location of a GPU 115 where a vGPU request orworkload 118 is being processed or executed. The workload data 126 caninclude a workload type for each workload 118 executed in the computingenvironment, as well as its current execution time, expected executiontime, actual total execution time once completed, wait time prior toplacement, suspended time, as well as each of the workload parameters128.

In step 509, the scheduling service 120 can train DNNs or vGPU placementneural networks 131 to maximize a composite efficiency metric 127 basedon the simulated or live workloads 118 assigned to vGPUs using the vGPUplacement models 129. In other words, the scheduling service 120 cantrain vGPU placement neural networks 131 to maximize the compositeefficiency metric 127 using the GPU data 125 and the workload data 126.Each of the vGPU placement neural networks 131 can be trained using thisinformation.

In step 512, the scheduling service 120 can determine whether additionalsimulated or live configurations are required. Following the simulatedexample, the GPU count in the computing environment 103 can be variedover a number of predetermined values including 3, 4, 6, 8, 9, 12, 16,20, and 24. Additional GPUs 115 can also be added to the simulation.Arrival rate can be varied from 20 arrivals per hour to 576 or morearrivals per hour in twelve or more steps. The simulator 121 cansimulate activity for 1 day, 2 days, 4 days, 7 days, and otherpredetermined time periods. If additional configurations remain to besimulated then the process can make configuration changes and move tostep 506. For live workload 118 configurations, the scheduling service120 can move to step 506 and monitor live workloads 118 until apredetermined set of a variety of different situations are experienced.No additional simulated or live configurations are required, then thetraining of the vGPU placement neural networks 131 can be consideredcomplete, and the process can move to step 515.

In step 515, the scheduling service 120 can generate a combined neuralnetwork based on a vGPU workload selector, such as the combined neuralnetwork selector 132, using the trained vGPU placement neural networks131. The combined neural network selector 132 can be generated based ona logical-OR combination operation, or a scaled add combinationoperation performed using trained vGPU placement neural networks 131.

A logical-OR combined neural network selector 132 can take a predictionvector for each of a number of vGPU placement neural networks 131. Ifany one vGPU placement neural network 131 selects a workload 118, thelogical-OR combined selector can select and place that task on a GPU 115queue. While the logical-OR combined selector can be simple, it tends tomark more tasks as selected than there are places available in thecloud.

A scaled-add combined neural network selector 132 can multiply theprediction vector for each of the vGPU placement neural 131 by aprecision of that vGPU placement neural network 131. This scales eachprediction vector by the probability that a workload 118 marked selectedby a vGPU placement neural network. The scaled prediction vectors forthe vGPU placement neural networks 131 are then added, and roundedand/or clipped to the range [0, 1], resulting in the scaled-add combinedneural network selector 132.

In step 518, the scheduling service 120 can implement the combinedneural network selector 132 for selection and placement of workloads 118on a vGPU-enabled GPU 115. For example, the scheduling service 120 canidentify vGPU requests for workloads 118 based on a live arrival queue.The scheduling service 120 can utilize current GPU data 125 and workloaddata 126 to identify required workload parameters 128. The workloadparameters 128 can be input into the combined neural network selector132. Using the combined neural network selector 132 can also indicatethat the workload parameters 128 are input into the individual vGPUplacement neural networks, and their outputs can be combined to selectone or more workloads 118 for placement. Thus, the scheduling service120 can select one or more workloads 118 for placement according to thecombined neural network selector 132. The selected workloads 118 can beplaced in a GPU queue of a vGPU-enabled GPU 115. The GPU 115 can executethe workload 118.

A number of software components are stored in the memory and executableby a processor. In this respect, the term “executable” means a programfile that is in a form that can ultimately be run by the processor.Examples of executable programs can be, for example, a compiled programthat can be translated into machine code in a format that can be loadedinto a random access portion of one or more of the memory devices andrun by the processor, code that can be expressed in a format such asobject code that is capable of being loaded into a random access portionof the one or more memory devices and executed by the processor, or codethat can be interpreted by another executable program to generateinstructions in a random access portion of the memory devices to beexecuted by the processor. An executable program can be stored in anyportion or component of the memory devices including, for example,random access memory (RAM), read-only memory (ROM), hard drives,solid-state drives, USB flash drives, memory cards, optical discs suchas compact discs (CDs) or digital versatile discs (DVDs), floppy disks,magnetic tape, or other memory components.

Memory can include both volatile and nonvolatile memory and data storagecomponents. Also, a processor can represent multiple processors and/ormultiple processor cores, and the one or more memory devices canrepresent multiple memories that operate in parallel processingcircuits, respectively. Memory devices can also represent a combinationof various types of storage devices, such as RAM, mass storage devices,flash memory, or hard disk storage. In such a case, a local interfacecan be an appropriate network that facilitates communication between anytwo of the multiple processors or between any processor and any of thememory devices. The local interface can include additional systemsdesigned to coordinate this communication, including, for example,performing load balancing. The processor can be of electrical or of someother available construction.

The flowchart shows examples of the functionality and operation of animplementation of portions of components described herein. If embodiedin software, each block can represent a module, segment, or portion ofcode that can include program instructions to implement the specifiedlogical function(s). The program instructions can be embodied in theform of source code that can include human-readable statements writtenin a programming language or in machine code that can include numericalinstructions recognizable by a suitable execution system such as aprocessor in a computer system or other system. The machine code can beconverted from the source code. If embodied in hardware, each block canrepresent a circuit or a number of interconnected circuits to implementthe specified logical function(s).

Although the flowchart shows a specific order of execution, it isunderstood that the order of execution can differ from that which isdepicted. For example, the order of execution of two or more blocks canbe scrambled relative to the order shown. Also, two or more blocks shownin succession can be executed concurrently or with partial concurrence.Further, in some embodiments, one or more of the blocks shown in thedrawings can be skipped or omitted.

Also, any logic or application described herein that includes softwareor code can be embodied in any non-transitory computer-readable mediumfor use by or in connection with an instruction execution system such asa processor in a computer system or other system. In this sense, thelogic can include, for example, statements including instructions anddeclarations that can be fetched from the computer-readable medium andexecuted by the instruction execution system. In the context of thepresent disclosure, a “computer-readable medium” can be any medium thatcan contain, store or maintain the logic or application described hereinfor use by or in connection with the instruction execution system.

The computer-readable medium can include any one of many physical media,such as magnetic, optical, or semiconductor media. More specificexamples of a suitable computer-readable medium include solid-statedrives or flash memory. Further, any logic or application describedherein can be implemented and structured in a variety of ways. Forexample, one or more applications can be implemented as modules orcomponents of a single application. Further, one or more applicationsdescribed herein can be executed in shared or separate computing devicesor a combination thereof. For example, a plurality of the applicationsdescribed herein can execute in the same computing device, or inmultiple computing devices.

It is emphasized that the above-described embodiments of the presentdisclosure are merely possible examples of implementations described fora clear understanding of the principles of the disclosure. Manyvariations and modifications can be made to the above-describedembodiments without departing substantially from the spirit andprinciples of the disclosure. All such modifications and variations areintended to be included herein within the scope of this disclosure.

Therefore, the following is claimed:
 1. A system comprising: at leastone computing device comprising at least one processor and at least onedata store; machine readable instructions stored in the at least onedata store, wherein the instructions, when executed by the at least oneprocessor, cause the at least one computing device to at least: identifyworkload data for a plurality of different datacenter configurationscomprising: a plurality of different graphics processing unit (GPU)counts, a plurality of different virtual GPU (vGPU) scheduling policies,and a plurality of different workload arrival rates, wherein theworkload data is generated by monitoring workloads executed or simulatedusing at least one computing environment configured according to theplurality of different datacenter configurations; train a plurality ofvGPU placement neural networks to maximize a composite efficiency metricbased at least in part on the workload data identified for the pluralityof different datacenter configurations, a respective one of the vGPUplacement neural networks comprising at least two sets of layers;generate a combined neural network selector based on the plurality ofvGPU placement neural networks; and utilize the combined neural networkselector to select a particular workload of at least one candidateworkload, and execute the particular workload in a particular computingenvironment using a particular vGPU-enabled GPU.
 2. The system of claim1, wherein the combined neural network selector selects the particularworkload of the at least one candidate workload based at least in parton a workload type of the particular workload.
 3. The system of claim 1,wherein the combined neural network selector selects the particularworkload of the at least one candidate workload based at least in parton a datacenter configuration of the particular computing environment.4. The system of claim 1, wherein monitoring the workloads comprisesexecuting a plurality of workloads comprising a plurality of differentsets of a plurality of workload parameters, wherein the plurality ofworkloads are executed using the at least one computing environmentconfigured according to the plurality of different datacenterconfigurations.
 5. The system of claim 4, wherein a particular parameterof the plurality of workload parameters comprises: a measure of thecomposite efficiency metric calculated for a workload scaled by ageometric mean of the composite efficiency metric calculated for arespective one of a plurality of workloads currently in an arrival queueawaiting selection for placement.
 6. The system of claim 4, wherein aparticular parameter of the plurality of workload parameters comprises:a workload type that indicates a purpose or activity performed.
 7. Thesystem of claim 4, wherein a particular parameter of the plurality ofworkload parameters comprises at least one of: a minimum graphicsmemory, and a graphics memory requested as a fraction of a total GPUmemory in the at least one computing environment.
 8. A method performedby at least one computing device executing machine-readableinstructions, the method comprising: identifying workload data for aplurality of different datacenter configurations comprising at least oneof: a plurality of different graphics processing unit (GPU) counts, aplurality of different virtual GPU (vGPU) scheduling policies, aplurality of different workload arrival rates, or any combinationthereof, wherein the workload data is generated by monitoring workloadsexecuted or simulated using at least one computing environmentconfigured according to the plurality of different datacenterconfigurations; training a plurality of vGPU placement neural networksto maximize a composite efficiency metric based at least in part on theworkload data identified for the plurality of different datacenterconfigurations, a respective one of the vGPU placement neural networkscomprising at least two sets of layers; generating a combined neuralnetwork selector based on the plurality of vGPU placement neuralnetworks; and utilizing the combined neural network selector to select aparticular workload of at least one candidate workload, and execute theparticular workload in a particular computing environment using aparticular vGPU-enabled GPU.
 9. The method of claim 8, wherein thecombined neural network selector selects the particular workload of theat least one candidate workload based at least in part on a workloadtype of the particular workload.
 10. The method of claim 8, wherein thecombined neural network selector selects the particular workload of theat least one candidate workload based at least in part on a datacenterconfiguration of the particular computing environment.
 11. The method ofclaim 8, wherein monitoring the workloads comprises executing aplurality of workloads comprising a plurality of different sets of aplurality of workload parameters, wherein the plurality of workloads areexecuted using the at least one computing environment configuredaccording to the plurality of different datacenter configurations. 12.The method of claim 11, wherein a particular parameter of the pluralityof workload parameters comprises: a measure of the composite efficiencymetric calculated for a workload scaled by a geometric mean of thecomposite efficiency metric calculated for a respective one of aplurality of workloads currently in an arrival queue awaiting selectionfor placement.
 13. The method of claim 11, wherein a particular set ofof workload parameters comprises at least one of: a workload type thatindicates a purpose or activity performed, a minimum graphics memory, agraphics memory requested as a fraction of a total GPU memory in the atleast one computing environment, or any combination thereof.
 14. Themethod of claim 11, wherein the at least one computing environmentcomprises a cluster of computing devices that provides host resourcescomprising a plurality of vGPU-enabled GPUs and at least one data store,wherein the at least one data store includes: a plurality of workloads,a scheduling service, and a plurality of vGPU placement neural networks.15. A non-transitory computer-readable medium comprising machinereadable instructions, wherein the instructions, when executed by atleast one processor, cause at least one computing device to at least:identify workload data for a plurality of different datacenterconfigurations comprising at least one of: a plurality of differentgraphics processing unit (GPU) counts, a plurality of different virtualGPU (vGPU) scheduling policies, a plurality of different workloadarrival rates, or any combination thereof, wherein the workload data isgenerated by monitoring workloads executed or simulated using at leastone computing environment configured according to the plurality ofdifferent datacenter configurations; train a plurality of vGPU placementneural networks to maximize a composite efficiency metric based at leastin part on the workload data identified for the plurality of differentdatacenter configurations, a respective one of the vGPU placement neuralnetworks comprising at least two sets of layers; generate a combinedneural network selector based on the plurality of vGPU placement neuralnetworks; and utilize the combined neural network selector to select aparticular workload of at least one candidate workload, and execute theparticular workload in a particular computing environment using aparticular vGPU-enabled GPU.
 16. The non-transitory computer-readablemedium of claim 15, wherein the combined neural network selector selectsthe particular workload of the at least one candidate workload based atleast in part on a workload type of the particular workload.
 17. Thenon-transitory computer-readable medium of claim 15, wherein thecombined neural network selector selects the particular workload of theat least one candidate workload based at least in part on a datacenterconfiguration of the particular computing environment.
 18. Thenon-transitory computer-readable medium of claim 15, wherein monitoringthe workloads comprises executing a plurality of workloads comprising aplurality of different sets of a plurality of workload parameters,wherein the plurality of workloads are executed using the at least onecomputing environment configured according to the plurality of differentdatacenter configurations.
 19. The non-transitory computer-readablemedium of claim 18, wherein a particular parameter of the plurality ofworkload parameters comprises: a measure of the composite efficiencymetric calculated for a workload scaled by a geometric mean of thecomposite efficiency metric calculated for a respective one of aplurality of workloads currently in an arrival queue awaiting selectionfor placement.
 20. The non-transitory computer-readable medium of claim18, wherein a particular parameter of the plurality of workloadparameters comprises: a workload type that indicates a purpose oractivity performed.